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AbstractÐMissing data are often encountered in data sets used to construct effort prediction models. Thus far, the common practice has been to ignore observations with missing data. This may result in biased prediction models. In this paper, we evaluate four missing data techniques (MDTs) in the context of software cost modeling: listwise deletion (LD), mean imputation (MI), similar response pattern imputation (SRPI), and full information maximum likelihood (FIML). We apply the MDTs to an ERP data set, and thereafter construct regression-based prediction models using the resulting data sets. The evaluation suggests that only FIML is appropriate when the data are not missing completely at random (MCAR). Unlike FIML, prediction models constructed on LD, MI and SRPI data sets will be biased unless the data are MCAR. Furthermore, compared to LD, MI and SRPI seem appropriate only if the resulting LD data set is too small to enable the construction of a meaningful regression-based prediction model.

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The key objective of this study was to understand the consequences of subjective ambivalence on customer satisfaction, loyalty, and the satisfaction-loyalty relationship. The conceptual and theoretical discussions were derived largely from recent research in social psychology and integrated with marketing literature on satisfaction and loyalty. Given that product evaluations are typically positive and extreme, these findings indicate a negative relationship between ambivalence and satisfaction. Even though a great deal of the variance in ambivalence is shared with satisfaction, ambivalence did prove to have an independent and direct effect on loyalty. Ambivalent consumers are not only less loyal because they are less satisfied, but for other reasons, as well. Ambivalence was not found to moderate the satisfaction-loyalty relationship. The results of the study underscore the importance of taking ambivalence into consideration when measuring satisfaction and modeling satisfaction-loyalty relationships.

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